Background: Oxygenation instability is not quantified or documented despite being common and correlated with neonatal morbidities, retinopathy of prematurity, and adverse 18-month outcomes.
Methods: We developed a five-type SpO histogram classification system based on the SpO difference within the 10-90th cumulative time percentile (A) and the time percentage with SpO ≤80% (B). In type 1, A is <5% and in type 5, A and B are ≥10%. We then studied consecutive 12-h SpO frequency histograms in all infants ≤34 weeks gestation receiving respiratory support on day 1, over 6 months.
Results: Six thousand and sixteen histograms were obtained in 73 infants, 28.9 ± 3.0 weeks gestation, and birth weight (BW) 1318.5 ± 495 g. All types were common and did not overlap. Type 3-5 ("unstable") histograms were more common in oxygen or any intubated support. Time in SpO <85% and <80% progressively increased in types 3-5. Among histograms in oxygen, the mean (±SD) of SpO medians was 92.8 ± 1.9. Infants ≤28 weeks exhibited three phases of SpO instability (stable-unstable-stable). Those developing unstable histograms during the first week received longer ventilatory support (median [IQR], 101 [66] vs. 62 [28] days) and supplemental oxygen (62.5 [72] vs. 40.5 [40] days), and more were on ventilatory support at 40 weeks (7/15 vs. 0/10).
Conclusions: Classified SpO histograms quantify and document SpO instability and identify early infants at risk of prolonged respiratory support, while median SpO does not.
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http://dx.doi.org/10.1038/s41390-019-0566-6 | DOI Listing |
Sensors (Basel)
January 2025
Space Robotics Research Group (SpaceR), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg.
Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification.
View Article and Find Full Text PDFPol J Radiol
December 2024
Nuclear Fuel Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Purpose: This study explored the use of computer-aided diagnosis (CAD) systems to enhance mammography image quality and identify potentially suspicious areas, because mammography is the primary method for breast cancer screening. The primary aim was to find the best combination of preprocessing algorithms to enable more precise classification and interpretation of mammography images because the selected preprocessing algorithms significantly impact the effectiveness of later classification and segmentation processes.
Material And Methods: The study utilised the mini-MIAS database of mammography images and examined the impact of applying various preprocessing method combinations to differentiate between malignant and benign breast lesions.
Lasers Med Sci
January 2025
Erzincan University, 24002, Erzincan, Turkey.
The aesthetic understanding has found its place in dental clinics and prosthetic dental treatment. Determining the appropriate prosthetic tooth color between the clinician, patient and technician is a difficult process due to metamerism. Metamerism, known as the different perception of the color of an object under different light sources, is caused by the lighting differences between the laboratory and the dental clinic.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
January 2025
Lund University, Centre for Mathematical Sciences, Division of Computer Vision and Machine Learning, Lund, Sweden.
Purpose: The survival rate of breast cancer for women in low- and middle-income countries is poor compared with that in high-income countries. Point-of-care ultrasound (POCUS) combined with deep learning could potentially be a suitable solution enabling early detection of breast cancer. We aim to improve a classification network dedicated to classifying POCUS images by comparing different techniques for increasing the amount of training data.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
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